Tools for data literacy: Engaging citizen scientists in analysis of mercury data from national parks across the U.S.

Abstract

The educational goals for many citizen science programs include improved science literacy, yet the analysis of scientific data is most often left to professional researchers. Working with data—evaluating the quality of... [ view full abstract ]

The educational goals for many citizen science programs include improved science literacy, yet the analysis of scientific data is most often left to professional researchers. Working with data—evaluating the quality of scientific information and the capacity to pose and evaluate arguments based on evidence—is foundational to the process of science and science literacy. The Maine Data Literacy Project was created to help non-scientists—specifically school teachers and students in citizen science-based Scientist-Teacher-Student Partnerships—develop tools and skills to use data they collect to productively answer their own questions. We identified challenges that non-scientists face when working with raw or messy data and developed a framework for teaching data literacy. Over the past year we have expanded our focus on citizen science and data beyond teachers and students to the Dragonfly Mercury project, which is a National Park Service-wide initiative engaging citizen scientists in ~50 national parks in the collection of dragonfly larvae from park waterbodies. The project brings together National Park Service staff, school groups and teachers, and older citizen scientists—many of whom ask for the project data and all of whom can benefit from an opportunity to develop skill in making sense of data they collect. Here we will present a set of data literacy tools developed by the Maine Data Literacy Project for an original audience of teachers and students together with versions of those tools adapted for the more diverse citizen scientist audience of the Dragonfly Mercury project. The set of three tools deal with key concepts of data sense-making: showing and describing variability; displaying, describing, and interpreting data in the context of a question; and explaining how the interpretation is supported by the evidence.